Integrating individual search and navigation behaviors in mechanistic movement models
Authored by Volker Grimm, Thomas Mueller, William F Fagan
Date Published: 2011
DOI: 10.1007/s12080-010-0081-1
Sponsors:
United States National Science Foundation (NSF)
Platforms:
No platforms listed
Model Documentation:
ODD
Flow charts
Mathematical description
Model Code URLs:
Model code not found
Abstract
Understanding complex movement behaviors via mechanistic models is one
key challenge in movement ecology. We built a theoretical simulation
model using evolutionarily trained artificial neural networks (ANNs)
wherein individuals evolve movement behaviors in response to resource
landscapes on which they search and navigate. We distinguished among
non-oriented movements in response to proximate stimuli, oriented
movements utilizing perceptual cues from distant targets, and memory
mechanisms that assume prior knowledge of a target's location and then
tested the relevance of these three movement behaviors in relation to
size of resource patches, predictability of resource landscapes, and the
occurrence of movement barriers. Individuals were more efficient in
locating resources under larger patch sizes and predictable landscapes
when memory was advantageous. However, memory was also frequently used
in unpredictable landscapes with intermediate patch sizes to
systematically search the entire spatial domain, and because of this, we
suggest that memory may be important in explaining super-diffusion
observed in many empirical studies. The sudden imposition of movement
barriers had the greatest effect under predictable landscapes and
temporarily eliminated the benefits of memory. Overall, we demonstrate
how movement behaviors that are linked to certain cognitive abilities
can be represented by state variables in ANNs and how, by altering these
state variables, the relevance of different behaviors under different
spatiotemporal resource dynamics can be tested. If adapted to fit
empirical movement paths, methods described here could help reveal
behavioral mechanisms of real animals and predict effects of
anthropogenic landscape changes on animal movement.
Tags
Genetic algorithm
Animal movement
patterns
spatial memory
Wandering albatrosses
Random-walks
Artificial neural-networks
Isopod hemilepistus-reaumuri
Systematic
search
Desert ants